Computer graphics has always been known for its
achievements in the field of arts and entertainment.
In recent years, science has gradually become
one of the areas in which computer graphics has
played an important role.
Visualization in Scientific Computing is now a
recognized discipline, which has given science a new
degree of understading in terms of
presentation of abstract data generated from
mathematical methods and simulations and
concrete data extracted from sources using real measurements.
Computer imagery in medicine has allowed physicians
to achieve a much higher degree of success in planning surgery
and other kinds of treatments. In particular,
radiotherapy has developed to a much higher level of
reliability and successful treatment by means of
computer graphics tools, which are used to aid
the planning process.

This work presents techniques developed for the application
in radiotherapy planning. Firstly, the foundations of
three-dimensional representation of computed tomography data
as well as investigation on some of its important topics
are presented.
Then, techniques for the outline of structures of interest
are investigated.
Geometric stylization of structures attempts to simplify
this process and make it more efficient.
Finally, the geometric relationship between various parameters
in three-dimensional radiotherapy planning is discussed,
and methods of three-dimensional display are presented.

This work presents a simplified,
scalable and flexible platform for
investigating visualization techniques
of brain activation regions,
detected in functional magnetic resonance images.
The tool addresses the main phases of this process, namely,
the reading of functional images,
the generation of activation maps and
the integrated visualization of activation maps
and anatomical data, using direct
volume rendering.

An Open Architecture for Volume Visualization of Medical Images Through
the Internet

by
Cilas de Freitas

Scientific visualization applied to medicine has
been strongly explored in the last years. The use of
the Internet as a means of communicating impelled the
development of distributed volume visualization
applications for medical images. Most of these
applications are based on strongly coupled architectures,
which implies in difficulties on issues such as
accessibility and integration with other applications.
This work presents an architecture for the development
of volume rendering services for medical images, using
open patterns for distributed computing via Internet.
This architecture allows for larger accessibility
and integration between the volumetric rendering services
providers and their customers.

Neurosurgeons need to adequately perceive the
spatial relationship between a brain tumour and
other healthy structures in order to conclude
what is the best place on the head to make the incision.
The presence of certain lesions, such as calcifications
caused by neurocisticercosis or some tumours such as
the meningiomas, can be perceived in computed
tomography exams. The correct delimitation of such
lesions is in many cases not feasible because in some
locations it is not possible to distinguish healthy
tissues from tumour tissues.
One way to address this issue is to approximate its
shape using a geometric solid such as the sphere,
which can be useful within the three-dimensional
perception context. The shape of the tumour tend to
conform with the shape of the sphere since it normally
grows uniformly in all directions. Another relevant
consideration is that it is more important for the
physician to know where the tumour is than to know
its exact shape.

This work describes a volumetric visualization platform,
based on a rendering technique called ray casting,
which accounts for the representation of tumours
by means of geometric defined spheres, allowing, thus,
the visualization of the stylized tumour within anatomy.

Magnetic resonance is an imaging modality with many applications
in medicine, particularly in brain studies. One advantage of its use
is the high contrast that it generates in soft tissues, allowing for
its use in the diagnosis of anomalies and in the planning of surgical
procedures.

The present work investigates methods of brain tissue segmentation that
use Markov random fields and genetic algorithms. A genetic algorithm
is employed to estimate initial parameters, aiming at improving the
segmentation process. The results thus obtained are compared with images
that were manually segmented by specialists. In addition, the results
of the segmentation process also make it possible to classify structures
and determine new parameters, which are useful in the creation of three
dimension images of the brain.

The identification of brain tumours is a complex task in the context
of diagnosis in medicine. However, significant advances can be
observed in this field due to the progress of medical image acquisition
techniques. On the other hand, computer graphics offers techniques
which provide excellent toos for manipulating these images.
In particular, the active contour model, also known as "snakes",
has been playing an important role in the process of segmentation and
extraction of structures of interest in clinical practice. This work
investigates the "snakes" method and proposes its use in conjunction
with genetic algorithms to address the limitations reported in the
literature. The results thus obtained demonstrate the potential of this
technique and encourage further research work in order to increase its
contribution in advanced medical diagnosis.

A significant advance in medical data acquisition technology has
taken place in the last two decades, providing benefits both in the
study and in the treatment of brain pathologies. The integration of
images of anatomic and functional exams allows for the mapping of
brain activation regions, important in neurosurgery and radiotherapy
planning. This work describes a flexible platform for the visualization
of multimodal volumes, with emphasis in the integration of functional
and anatomic information, including modalities such as Magnetic
Resonance Imaging (MRI), Computed Tomography (CT) and Functional
Magnetic Resonance Imaging (fMRI).

In this scenario, specific issues are addressed, namely, brain
segmentation and the attenuation of irregularities in the cortex
surface using a morphological close algorithm. The integration and
presentation of images are performed based on the raycast algorithm.
The results obtained show contributions which are important in the
development of tools for multimodal volumetric visualization.

This work describes a visualization tool for
analytical data which allows the user to select
regions of interest within the data by means
of mathematical formulations, referred to as filters.
The mathematical formulations which can be used to
compose the filters include functions, matrices,
equations systems, among other algebraic elements of Mathematics.
This work proposes thus a higher level of abstraction in
the use of Mathematical Formulated Filters so that
their definition can be made using a language as
natural as possible. The results which can
be achieved by the proposed tool are shown through
visualizations generated from a case study in the
area of heat transfer.
Functional magnetic resonance imaging (fMRI) allows
for the identification of regions of the human brain
associated with certain sensory, motor or cognitive tasks.
Integrated visualization of functional information and
anatomical structures of the human brain is an important
field in volumetric visualization applied to Medicine.